In the past, multi-sensor navigation systems have been used with much success. These multi-sensors typically use global positioning system (GPS) receivers in combination with other inertial sensors to generate position information. However, due to the inadequate signal-in-space integrity currently provided by GPS for civil aviation, it is generally necessary to also employ real-time fault detection and exclusion (FDE) schemes, especially when the multi-sensor system is used for primary means of navigation. Since the current assumptions about the GPS constellation and its measurement accuracy often do not provide high enough availability of FDE to meet the primary means objectives for all phases of flight, integration of GPS with inertial sensors for integrity monitoring has received considerable attention. See Brenner, M., “Integrated GPS/Inertial Fault Detection Availability”, ION GPS-95, Palm Springs, Calif., Sep. 2–15, 1995, pp. 1949–1958, hereafter referred to as “Ref [1].” Also see Vanderwerf, K., “FDE Using Multiple Integrated GPS/Inertial Kalman Filters in the Presence of Temporally and Spatially Correlated Ionospheric Errors”, ION GPS-2001, Salt Lake City, Utah, Sep. 11–14, 2001, pp. 2676–2685, hereafter referred to as “Ref [2]”. Also see Diesel, J., Luu, S., “GPS/IRS AIME: Calculation of Thresholds and Protection Radius Using Chi-Square Methods”, ION GPS-95, Palm Springs, Calif., Sep. 12–15, 1995, pp. 1959–1964, hereafter referred to as “Ref [3].” Prior art GPS integrity algorithms can be classified into two broad categories based on the method employed for computing the navigational solution—snapshot approaches and filtered approaches. Snapshot approaches are generally based on least-squares (LS) methods, while filtered approaches generally utilize multiple Kalman filters with different fault hypothesis models.
In addition to the snapshot vs. filtered classification, the prior art GPS integrity algorithms can also be classified into two different categories based on the characteristics of their test statistics for the FDE function—range domain methods vs. position domain methods. For example, the solution separation algorithms described in Refs. [1], [2], and in Brumback, B. D., Srinath, M. D., “A Chi-Square Test for Fault Detection in Kalman Filters”, IEEE Transactions on Automatic Control, Vol. AC-32, No. 6, June, 1987, pp. 552–554, hereafter referred to as “Ref. [4]” are position domain methods, and the measurement residual algorithm in Ref. [3] is a range domain method.
The prior art multi-sensor systems and FDE algorithms have several common characteristics and requirements. The FDE function is typically required to ensure the integrity of the navigation solution and prevent the use of hazardous and misleading information. The prior art FDE function typically consists of two distinct parts—fault detection and fault exclusion. The purpose of prior art fault detection has been to detect the presence of an unacceptable error, and the purpose of the typical prior art fault exclusion has been to identify and exclude the culprit causing the unacceptable error with high confidence. The FDE function is also often required to provide a statistical bound, horizontal protection level (HPL), which the FDE function often guarantees that the horizontal position error can only exceed it prior to fault detection within the specified probability of missed detection (PMD).
After a fault is detected in prior art systems, the HPL is often operationally of little value because HPL no longer bounds the true error at the specified statistical level. The horizontal uncertainty level (HUL) can provide a bound on the horizontal position error after fault detection and before the correct fault exclusion can be performed. Thus, it typically enables the pilots or airborne equipment to determine whether the navigation solution is still acceptable at a given phase of flight. The operational differences between HPL and HUL are discussed in Young, R. S. Y., McGraw, G. A., Driscoll, B. T., “Investigation and Comparison of Horizontal Protection Level and Horizontal Uncertainty Level in FDE Algorithms”, ION GPS-96, Kansas City, Mo., Sep. 17–20, 1996, pp. 1607–1614, hereafter referred to as “Ref. [5].”
While these prior art multi-sensor systems and FDE algorithms have enjoyed success in the past, they have some drawbacks in certain circumstances. Often these systems fail to provide a sufficiently precise probability level for the detection threshold and HPL. Additionally, in some prior art systems, it is necessary to make real GPS measurements to achieve a requisite level of predictability.
Consequently, there exists a need for improvement in the FDE algorithms for multi-sensor navigation systems.